Towards more reliable gambling cost estimates, a population-based study with register-linkage

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Latvala, Matthew Rockloff, Matthew Browne, Tomi Roukka, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4482877/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Aug, 2025 Read the published version in BMC Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Although past research has shown a strong association between gambling participation and harms, relatively few studies have attempted to quantify the cost of these harms to society. The need to quantify costs has been identified in several countries, however, no consensus exists in the field of gambling studies on how one should estimate them. Methods: Three methods were selected for costs calculations: Causality adjustment factors (with two variations: CAF 80%/ CAF 50%), Excess costs, and a method based on Bayes' Theorem. Our purpose was not to examine the overall costs of gambling, but rather to evaluate different approaches for one specific outcome. Our focus was on indirect costs relating to productivity losses associated with long-term work disability in those aged 18–64 years who had experienced gambling problems before long-term work disability had started. Work disability was operationalized as the net days of sickness absence and disability pension. The study used population-based Gambling Harms survey and the survey data were linked with the register data. Results: These three methods gave very different estimates on costs relating to productivity losses associated past problem gambling. The Excess cost method gave the highest estimate of 127.04 million euros (25.94 Int$/adult) followed by the Causality adjustment method (CAF80%) of 56.97 million euros (11.63 Int$/adult) and CAF 50% with 35.61 million euros (7.27 Int$/adult). The method based on Bayes' Theorem gave the lowest estimate of the cost at 8.57 million euros (1.75 Int$/adult). Conclusions: Methods commonly used in gambling cost studies yield higher estimates of gambling costs when arbitrary causality adjustment methods are used. Bayes’ theorem allows leveraging data on temporal patterns of gambling problems to estimate the plausible proportion where gambling is the precipitating factor for the experienced harm, rather than the other way around. Additionally, costs could be presented as Int$ per adult using the PPP exchange rate to facilitate the comparison of gambling costs between countries. costs societal costs problem gambling population survey register data methods Background Gambling availability has increased during previous decades because of growth in internet sites which have enabled gambling from home, work and nearly anywhere. Gambling is a common form of entertainment in many western countries, and most of adults have participated in it at least sometime of their life (1,2). However, for some people gambling can cause serious health, financial and interpersonal harms (3,4). Financial harms, such as lost savings and debt problems, are the most common harms reported by gamblers (5). Gambling-related financial harms can in turn increase psychological distress, substance use, relationship problems, crime, and even suicidality (4,6). Based on the Lotteries Act, the aims of the Finnish gambling monopoly system are to prevent and reduce gambling-related financial, social and health-related harm (7). There is strong evidence that different types of health problems are linked with problem gambling, and its most severe form, gambling disorders (GD) (e.g. 8,9). It is clear that more severe morbidity tends to cause higher expenses. Accordingly, the need to calculate gambling costs has been identified in several countries (10). Browne and colleagues (11) found in their systematic literature review on gambling-related harms and costs that only three out of 36 gambling-prevalence studies published in 2010–2016 reported gambling costs. The most cited and emulated research in the field of gambling costs studies found that the overall gambling costs in Australia were 1.8–5.6 billion Australian dollars (AUD) between 1997 and 1998 (12). A more recent study from Australia estimated cost to be $7 billion in 2014–2015 in Victoria (11). Other studies conducted in Europe (8,13–18) and Asia (19) have also identified significant costs to society. However, due to a range of differences in gambling environments and policies, and different methods behind these calculations, comparison of costs between countries is not straight-forward. There is also a lack of consensus on what should be included when calculating gambling costs (20,21). There has been also discussion whether intangible (pain and suffering) costs, which are more difficult to evaluate in monetary form, should be included in calculations. While these questions are not easily resolvable, it is nevertheless advisable to pay attention to the methods how gambling costs are calculated. Regardless of past research showing a strong association between gambling and harms, relatively few studies have examined costs of these harms to society. Thus, we will start by reviewing methods used in the gambling cost studies and evaluate them critically. Our emphasis is on tangible costs, where a monetary value can be most readily applied. This is not to deny the importance of other costs, but rather to make our evaluation more tractable. This review informed our evaluation of alternative methods for calculations that follow later in this paper. Gambling and health issues There is strong evidence on that psychiatric and substance used disorders are linked with gambling problems (e.g. 22–28). A Swedish registry-based study indicated that 73 percent of those who had a diagnosed GD had other co-occurring psychiatric diagnosis (23). A meta-analysis showed that the highest mean prevalence for co-occurring psychiatric disorders were for nicotine dependence (60.1%), followed by a substance use disorder (57.5%), mood disorders (37.9%) and anxiety disorders (37.4%) (25). Overall, self-rated health is found to be lower among those with problem gambling than non-gambling counterparts (29,30). There is evidence of comorbidity between problem gambling and poor physiological health (9,29–32). Problem gambling is linked to poor diet, low physical exercise, and obesity (9,29). Moreover, those with gambling disorder are more likely than low-risk individuals to be diagnosed with tachycardia, angina, cirrhosis, and other liver disease (31). In addition, problem gambling is associated with headache, fatigue, and sleeping problems (33). Among women, problem gambling is linked to bronchitis, fibromyalgia, and migraine (34) and among older adults, with heart conditions (32). There is also evidence that individuals with GD have an increased risk of receiving sickness allowance (35) and increases risk of work disability (36). In Finland the most common reasons for receiving sickness allowance and disability pension are mental and behavioral disorders. Mental and behavioral disorders account for over half of disability pensions and over one third of sickness allowances (37). The likelihood of transitioning to disability pension increases significantly after the first year of receiving sickness allowance (38), with the risk escalating further with the duration of the sickness absence (39). Disability pension poses a substantial financial burden on society due to the low rates of recipients returning to work (40). Cost of illness (COI) studies Cost of illness (COI) is defined as the value of the resources that are lost because of a health problem. COI studies assess the economic burden of health problems on the population overall (41). COI studies can be used to draw public attention to particular health issues and to provoke policy debate (42). They can also guide planning of healthcare and preventive services, interventions and the evaluation of different policies (43–45). COI studies can be based on the incidence or prevalence of the disease. In an incidence-based approach, the new (incident) cases are measured, while the prevalence-based approach measures the existing (new and pre-existing cases) cases over a specified period, usually one year. It is considered that prevalence-based approach is more appropriate for assessing total current economic burden of a health problem whereas an incidence-based approach is more useful for estimating the expected impact in the future (46). In this study we applied prevalence-based approach. COI studies commonly include related healthcare costs and other resources used (direct costs), losses of productivity related to morbidity and mortality (indirect costs), and the losses in quality and length of life (intangible costs) (43). Traditionally these effects of health problem are converted into monetary values wherever possible (41,44). However, intangible costs are not usually monetized; instead they are expressed measures, such as disability-adjusted life-years (DALYs) or quality-adjusted life-years (QALYs) (41). Although debate regarding the most appropriate approach to calculate productivity losses is still on-going, we opted to use the human capital approach (47). The value of the human capital is estimated based on the value of an average individual's future earnings. On the other hand, the fractional costs approach attributes only 80% of losses to avoid potential overestimation of indirect costs (48). This discounting is intended to account for the fact that it cannot be assumed that the condition plays a 100% causal role in impacting earnings. However, given the true causal role is unknown, any such discounting is unavoidably somewhat arbitrary. Methods commonly used in gambling cost studies Causality adjustment factors As mentioned earlier, the most emulated method is from the Australian Productivity Commission (12), and it is still widely used. The main principle behind this method is that cost of the harm per gambler is multiplied by the number of people experiencing the harm. Because the causality between gambling and harms is often unknown, costs have been discounted with a ‘causality adjustment factor’ (CAF) (12). This based on expert opinions suggesting that approximately 20% of individuals struggling with gambling issues would have encountered similar personal and family-related consequences even in the absence of gambling problems. In practice, this entails that costs are discounted by 20 percent (multiplied by 0.8), similar to the fractional cost approach described above. In some studies costs were discounted by as much as 50 percent, as there were no or only little evidence on the direction of causality (8). The costs are calculated as following, Cost of harm = N G * CAF* C, where (1.1) N G = estimated number of gamblers with a particular harm CAF= causality adjustment factor C=unit cost of individual with harm. Despite acknowledging the limitations of discounting costs by an arbitrary CAF, many studies have used the method (18). Excess costs Three research reports conducted in Britain (15,17,49) quantified costs by calculating the excess costs between gamblers compared to the non-gambler population and then multiplying this excess cost by unit cost of individual harm as following: Cost of harm = (N G - N P ) * C, (1.2) where N G = estimated number of gamblers with a particular harm N P = estimated number of gamblers expected to have harm if they had same rate of harm as the general population C =unit cost of individual with harm. This Excess cost does not use actual data from real cases. Estimated number of gamblers expected to have experienced a particular harm is calculated by multiplying prevalence rate of a particular harm by the prevalence figures for problem gambling. This gives an estimate of the number of gamblers with specific harms in the general adult population. Multiplying these figures by the estimate of association between gambling and these specific harms (adjusted-OR) produces the number people of experiencing problem gambling who are expected to have these particular harms. The difference of estimated number and expected number of specific harms, (NG − NP), will give an estimate of the number of people with a particular harm associated with problem gambling only. Method based on Bayes' Theorem Bayes' Theorem is a mathematical formula used for calculating conditional probabilities (50). In its simplest form, Bayes' Theorem is expressed as: P(A∣B) is the posterior probability of event A given that event B has occurred. P(B∣A) is the likelihood of event B occurring given that event A has occurred. P ( A ) and P(B) are the prior probabilities of events A and B respectively. We can use Bayes' Theorem to estimate the proportion of those experiencing long-term work disability who likely had their problem gambling lead to the work disability: P(gambling led to pension) = [P(past gambling problems | long-term work disability) * P(long-term work disability)] / P(past gambling problems) Where: P(past gambling problems | work disability) is the rate of gambling problems before long-term work disability had started among those who are currently on long-term work disability P(disability pension) is the overall rate of long-term work disability in the population in 2016 P(past gambling) is the rate of gambling problems before long-term work disability had started in the general population If P(past gambling problems | work disability) is significantly higher than P(past gambling), it suggests that past gambling problems increase the likelihood of long-term work disability. Formula 1.3 provides the proportion of people with long-term work disability whose gambling issues likely occurred before and played a role in their extended work incapacity. To conclude, during the past decade there has only been limited discussion of the appropriate method for calculating gambling costs. Based on the extant literature, we have selected three methods, including the Causality adjustment factors (with two variations), the Excess costs and the Method based on Bayes' Theorem, and use them in our gambling costs calculations. Our calculations focus on the indirect costs regarding to long-term work disability, in those aged 18–64 years who experienced gambling problems before long-term work disability started. This is a specific context for gambling cost assessment for which high quality data is available, should provide good scope for evaluating alternative costing methods. We do not mean to suggest, however, that these are the only costs – financial or otherwise – that can be due to gambling issues. Rather, the goal of this study is to provoke discussion on methods used in gambling cost studies and hopefully assist the formulation of a consistent approach for cost calculations in the field of gambling studies. Methods Data and participants This study utilized the population-based Gambling Harms survey, which was conducted by the Finnish Institute for Health and Welfare among residents of three geographical areas in Finland: Uusimaa, Pirkanmaa and Kymenlaakso in 2016 (51). People in these areas comprise 42% of the Finnish population. Statistics Finland collected the data between January and March in 2017, but all the gambling-related questions reflected activity in the calendar year 2016. Participants had to be 18 years old or over, and they needed to understand Finnish or Swedish, as the online and postal surveys were available in these two official languages. Institutionalized persons (prisoners, infirmed, etc.) were excluded from the survey. From the population register, 20,000 potential participants were randomly selected. When non-eligible (n = 67) participants were excluded (e.g., illness, living abroad), the study sample size was 19,933 persons. Overall, 7,186 adults participated in the study, giving a response rate of 36.1%. Potential participants were sent an invitation letter at their home addresses, wherein they received written information about the study and the principles of voluntary participation. They were informed that participating the study involved the register linkage, whereby government-held information on them would be combined with their survey results. Information was also provided about the registrars and a list of the register-based variables used in analyses. They also were informed that their responses would be used for scientific purposes. The invitation letter included a link to the online survey and personal participation code. Most respondents (71%, n= 5,084) participated using the online survey. Respondents’ ages ranged from 18 to 94 years (M=50.5, SD=18.8) and 47.7% of them were male. Based on the sample frame, respondents who were older, women, married, or had higher education were more willing to participate than men, younger, divorced, or single, or those with lower-than-average education (51). The respondents most willing to participate were in the age groups 55–64 and 65–74, while the youngest age group, 18–24-year-olds, and particularly men, were least responsive (51). For the purposes of this study, only working age respondents (18–64-year-olds) were selected (N = 5,122). The data were weighted on gender, age, and region of residence. Measures from the survey Perceived past problem gambling was evaluated using a single question: “Do you feel that gambling has ever been a problem for you?”. If they answered yes, then they were asked the most recent year, when they felt that was the case. Those respondents who felt that they had experienced gambling problems before long-term work disability had started, were treated has having experienced past problem gambling. Measures from the register data The survey data were linked with the register data administered by Statistics Finland. Register data included information on sociodemographic measures (sex, age, education and disposable income), and information on disability pensions. Education . Education was based on the highest degree attained and followed the International Standard Classification of Education. Those who had missing values were coded as ‘low/unknown education’ (below Level 3). Levels 3 and 4 were classified as ‘medium education’ and Level 5 or higher as ‘high education’. Disposable income per year . Disposable income is obtained by adding current transfers receivable to primary income and by deducting all current transfers payable (Table 1). Incomes are rounded to the nearest hundred euros. In analyses disposable income was divided to quartiles. Long-term work disability . Based on the Finnish social security system, if a person is incapable for work, he/she can receive sickness allowance as compensation for loss of income for maximum of 300 working days (about a year). Sickness allowance is available after completing a waiting period, which consists of the first day of illness and the following nine working days. After receiving sickness allowance for 150 working days, a person will be informed about the availability of rehabilitation and the process of applying for a pension. If rehabilitation does not restore or improve the work ability, a person may be entitled to a disability pension (52). In the register data, all sickness days compensated to receiver or employer were presented. As the sickness allowance is available after a waiting period, nine days were added to the length of compensated sickness days. Days were also divided by 30 to get the number of sickness months. If respondents had received a disability pension in 2016 a value “1” was given to him/her and “0”, if not. There were also variables, which indicated the year and month when disability pension had started. As our purpose was examine only productivity losses in 2016, pensions that had started before year 2016 we given value 12 (12 months in year). To calculate the total months of long-term work disability, sickness and disability months were summed up. Long-term disability was dichotomized, and respondents were given value of 1 if she/he had been 90 net days or more on sickness absence or disability pension during 2016, as in a previous study (37). Based on the average length of long-term work disability in months (dm avr ), the average cost for productivity loss associated with one persons’ absence from work because of long-term work disability (C pld_avr ) was calculated as follows: C pld_avr = dm avr *w m +0.2(dm avr *w m ), where (1.3) dm med = average length of long-term work disability in months w m = average of monthly salary estimated by calculating the mean of 16 years' salaries Average monthly salary was estimated by calculating the mean of 16 years' average wages. This was € 2771.66 between 2000-2016. An estimate of the value of the productivity loss was obtained by adding the side costs of the salary, i.e. the employer's social insurance fees, the amount of which is approximately 20 percent of the salary (Lappo, 2023). The average length of work disability in months was 10.8. This gave a unit cost of € 35,920.71 for productivity loss due to work disability. This average cost was applied in Causality adjustment factor-method and in Excess cost-method. In method based on Bayes’ theorem dm med was replaced with actual length of disability as follows: C pl_avr = dm*w m +0.2(dm*w m ), where (1.4) dm = length of work disability in months Statistical analysis Respondents’ sociodemographic factors, gambling severity, and the percentage of those respondents on disability pension are presented in Table 1. Causality adjustment factor -method and Excess cost -method : First, logistic regression models were created, where age, sex, disposable income and education were set as covariates to examine whether there were statistically significant association between past problem gambling and long-term work disability (Supplementary material, Table S1). All respondents who were on long-term work disability were given value ‘1’ and all others value ‘0’. Long-term work disability was dependent variable. Recreational gambling was set as the reference group. Subsequently, formula 1.1 and 1.2 were used to calculate the productivity losses associated with past problem gambling. Both causality adjustment factors 0.8 and 0.5 were used. The results are presented in Table 2 in euros and in 2016 international dollars (Int$) per adult (person age of 20 or more) using the Purchasing Power Parity (PPP) exchange rate (53). The PPP exchange rate between two countries signifies the rate at which the currency of one country must be converted into that of another to maintain parity in purchasing power. In the World Economic Outlook (WEO) online database, the implied PPP conversion rate is expressed as national currency per current international dollar (Int$) (54). Int$ are a hypothetical unit of currency used in international comparisons of purchasing power. Method based on Bayes' Theorem : Productivity loss for each respondent was calculated by formula 1.3. This formula provides the proportion of people with long-term work disability whose gambling issues likely occurred before and played a role in their extended work incapacity. The average cost of productivity loss associated with long-term work disability among people with past PG is presented in Table 2 in euros and in Int$. Statistical analyses were done using IBM SPSS Statistics for Windows version 27.0. Results Of the respondents, 2.8% had experienced past problem gambling. Further, 5.1% of the respondents were on long-term work disability (Table 1). Of the respondents who experienced past problem gambling, 11.5% were on long-term work disability (Table 1). In logistic regression models where respondents’ sex, age, disposable income, and education were adjusted, statistically significant association between long-term work disability and past problem gambling were seen (Supplementary material, Table S1). Cost based on Causality adjustment factor -method It was estimated that in study regions there were 37,405 people experiencing past problem gambling (Table 2). Among those on long-term work disability, 5.3% experienced past problem gambling (N = 1,982). Based on the CAF method (formula 1.1) and estimation on a unit cost of € 35,920.71 for productivity loss due to long-term work disability, this would mean that productivity losses associated with past problem gambling would be 56.97 million euros (11.63 Int$/adult). If CAF was replaced with value 0.5 the productivity losses would have been 35.61 million euros (7.27 Int$/adult) for past problem gambling (Table 2). Cost based on Excess cost -method Multiplying the prevalence rate of being on long-term work disability in the survey region (6.1%) by the estimated number of people experiencing past problem gambling (A=37,405) gives an estimate of expected number of past gamblers on long-term work disability if they had same rate of harm as the general population (N p =2,282) (Table 2). When this figure is multiplied by the estimate of association between past gambling and long-term work disability (OR problem =2.55), it yields the estimated number of gamblers receiving disability pension (N h =5,818). The difference of the estimated number and the expected number gives an estimate of the number of people on long-term work disability associated with past problem gambling only (N=3,537). When this number is multiplied by the average cost of productivity loss due long-term disability, it gives the cost of 127.04 million euros (25.94 Int$/adult) for past problem gambling (Table 2). Cost based on Bayes' Theorem The percentage of gambling problems before long-term work disability had started among those who were currently on long-term work disability was 5.3 (Table 2). This was significantly higher than percentage of gambling problems before long-term work disability had started in the general population (χ = 8.22(1), p=0.004). The average productivity loss due to long-term work disability among people experiencing past gambling problems was € 37,595.28. Based on the Bayes' Theorem (formula 1.3) we get the estimate for the proportion of those on long-term work disability who likely had their gambling lead to the disability. In approximately 11.5% of long-term work disabilities, gambling issues likely preceded and thus contributed to disability. Thus, productivity losses associated with past problem gambling are estimated at 8.57 million euros (1.75 Int$/adult). Discussion Our study examined the economic burden associated with past problem gambling using three different methods among Finnish population aged 18–64 years. The study focused on indirect cost regarding productivity losses due to long-term work disability among people experiencing problem gambling before work disability started. As mentioned above, our purpose was not to examine the overall costs of gambling, but rather to evaluate different approaches for one specific outcome: productivity losses resulting from the uptake of disability pensions. Two of the methods, the CAF -method and the Excess cost method, are most used in gambling field, whereas the method based on Bayes’ theorem was a novel way to estimate gambling cost. Overall, these three methods gave very different estimates on costs associated with past problem gambling. The Excess cost method gave the highest estimate of 127.04 million euros (25.94 Int$/adult), followed by the Causality adjustment method (80%) showing 56.97 million euros (11.63 Int$/adult), and the CAF 50% estimated at 35.61 million euros (7.27 Int$/adult). This is a natural consequence of the arbitrary choice for the discounting factor. The method based on Bayes’ theorem gave the lowest estimation of the cost, calculating 8.57 million euros (1.75 Int$/adult), which is over 14 times lower than the highest estimate. Thus, it appears that CAF methods or Excess cost methods are generally likely to give higher estimates on gambling costs. Determining the extent to which gambling is the direct cause of various social and economic harms is challenging. The CAF methods can lead to inflated estimates of gambling-related costs as it attempts to adjust for the proportion of observed harms attributable to gambling just by multiplying cost by 0.8 or by 0.5. This adjustment can be imprecise, often resulting in higher cost estimates that may not accurately reflect the true impact of gambling. Similar problems arise with the Excess Cost method, as it estimates the number of people experiencing a particular harm associated with problem gambling by comparing the observed number of affected individuals to the expected number in the general population. This approach can lead to inaccuracies because it assumes that any excess harm among gamblers is directly attributable to gambling, without adequately accounting for other factors that might contribute to these harms. Further, both Excess cost and CAF method often rely on average values, which can obscure the significant variations in gambling behavior and its impacts. Utilization of the Bayes’ theorem allowed us to leverage the data on temporal patterns of gambling problems to estimate the plausible proportion where gambling was the precipitating factor for long-term work disability, rather than the other way around. Using this approach, we estimated that approximately 11.5% costs related to long-term work disability was due to past gambling problems. Overall, reliable data are required for reliable estimates on gambling related costs. The data must be from a representative sample of the general population. High response rates are crucial for being able to generalize about the population. This is especially important to consider since there are declining trends in response rates. Low response rates may cause biased findings. Methods for controlling bias would offer useful information about the impact of non-response on the results (55). Information on gambling related costs should rely on multiple data sources. Besides population-based surveys, also health register data and player account data should be utilized. An especially crucial aspect to explore would involve examining the trajectories of gambling through the analysis of longitudinal data. While our study examined only problem gambling, it is essential to recognize gambling as a continuum when examining gambling-related costs. We should not solely focus on problem gambling but consider the entire spectrum. It is true that the most serious forms of harms, like suicide attempts, large debts, and criminal activity are more common among people who are gambling at the highest problem level. However, a greater number of cases, and thus higher costs, come from at-risk gamblers within a population. Although they are harmed less on an individual level, they are much more prevalent than people gambling at problem level (56). Further, problem gambling is not static condition; individuals can move along a continuum from less severe levels of gambling behavior to more problematic ones, and vice versa. Factors such as personal circumstances, social influences, access to gambling opportunities, and changes in mental health can all play a role in this progression or regression along the continuum. Future studies should also recognize different subgroups. Based on several review articles and meta-analyses, several sociodemographic risk factors, such as male gender, young and old age as well as low socio-economic status are associated with problem gambling (57–59). People with lower income spend a relatively larger proportion of their household income on gambling. Similarly, the unemployed, people laid off from work and people with lower education spend a larger proportion of their income on gambling (60,61). There are several limitations in our study. First, our emphasis was not on the total cost of gambling. Instead, our calculations covered one element of productivity losses. We did not have information on severe harms, such as suicidal deaths or incarcerations related to gambling, which would further impact on productivity losses. It is important to note that we have no information on costs associated with presenteeism, and thus productivity declines due to gambling problems may reflect low output amongst workers suffering gambling-related problems. Thus, these results may underestimate some aspects of productivity losses associated with gambling. There were also quite few people experiencing past problem gambling and simultaneous on long-term work disability. This enabled us to concentrate solely on new disability pension that started on 2016. In addition, we cannot exclude confounding factors through differences in unobserved behaviors. The effect of gambling could, therefore, be under- or overestimated, for example by not adjusting for any concomitant diseases, other risk behavior, or socio-demographic characteristics. To minimize potential confounding, further studies should control at least for some concomitant diseases and, for example, high levels of alcohol consumption that often accompany gambling problems (26). We had also some scarcity in register data, as education records had no information on the lowest levels of education. Also, we did not have information on past gambling severity for the person reported having experienced problem gambling. Further we did not consider any age, sex or socioeconomic differences in salaries or the fact that work productivity decreases with age. Thus, in these various respects our calculations were simplified. In terms of sampling, a further limitation was that we used a cross-sectional sample, and therefore definitive conclusions regarding causal relationships between productivity losses and gambling were not possible. For example, disability pensions could cause increased problem gambling behavior, rather than the other way around because it is plausible that individuals receiving disability pensions may have more free time for gambling due to being unable to work. Some individuals on disability pensions may see gambling as a relief from negative affect or a potential way to supplement their income, especially if they face financial difficulties. Further, we cannot conclusively assert that problem gambling was the sole reason for long-term work disability, unlike the more evident connection observed in cases of alcohol use. In the context of alcohol use, there is a clearer physiological link between excessive consumptions and health conditions potentially leading to disability. However, determining the direct association between problem gambling and long-term work disability may involve more nuanced considerations and additional factors. Finally, the response rate of this study (36%) was relatively low, and thus our results can reflect self-selection biases. Regardless of these limitations, the method based on Bayes’ theorem, which relied on subject-level data has the advantages when consequences associated with gambling are considered. The former methods used have relied on average costs and arbitrary ways to adjust the unknown direction of causality, which give higher estimates on gambling costs. Although, method based on Bayes’ theorem gave lower estimates, it nevertheless implies that gambling imposes a significant societal health burden. Conclusions Our results emphasize that “the method matters” since the estimates of gambling related costs greatly vary depending on what method is chosen. In the absence of a better estimate, gambling cost studies have commonly used “causality adjustment factors”, and to some extent it has become as a standard in the field of gambling cost studies, at-least amongst the rare gambling cost studies conducted so far. However, a few studies have diverged from this standard and used the Excess cost method. It appears that methods commonly used in gambling cost studies give higher estimations on gambling costs when these arbitrary causality adjustments methods are used. So far, no consensus exists in the field of gambling studies on how one should estimate gambling related costs. However, utilization of Bayes’ theorem would allow to leverage the data on temporal patterns of gambling problems to estimate the plausible proportion where gambling is the precipitating factor for the experimented harm, rather than the other way around. One practical solution that could facilitate the comparison of gambling costs between countries is, that the costs would be presented as Int$ per adult using the PPP exchange rate. Importantly, such a consensus would be crucial in order to generate comparable and reliable figures, and overall stronger evidence for informed decision making related to gambling policies. Abbreviations CAF Causality adjustment factors COI Cost of illness DALYs Disability-adjusted life-years GD Gambling disorder Int$ international dollars PPP Purchasing Power Parity QALYs Quality-adjusted life-years WEO World Economic Outlook Declarations Ethics approval and consent to participate: Throughout the research process, basic principles of the research ethics were applied (The World Medical Association’s Declaration Helsinki 2004). The research protocol was approved by the Ethics Committee of the Finnish Institute for Health and Welfare (Statement THL/1390/ 6.02.01/2016). Statistics Finland gave the permission to use the register-based measures, and their rules and instructions were followed. All the statistical analyses on register data were conducted in a protected environment using a remote access system, and the results were screened by Statistics Finland before publishing. While presenting the results, the data were treated with strict anonymity to safeguard the identities of the respondents. Consent for publication : Not applicable Availability of data and materials: The survey data analyzed during the current study are available in the [Finnish Social Science Data Archive] repository, [https://urn.fi/urn:nbn:fi:fsd:T-FSD3261]. Register data is only available by permission from Statistics Finland. Competing interests: The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: Gambling Harms survey and daily work of the authors TAL, TR and AHS at the Finnish Institute for Health and Welfare, Finland, was funded by the Ministry of Social Affairs and Health, Finland, within the objectives of the §52 Appropriation of the Lotteries Act. The funders have had no role in the study design, analysis, or interpretation of the results of the manuscript or any phase of the publication process. Future opportunities of the authors are not contingent upon the results of the research. Authors' contributions: TAL, TR, TPL and AHS conceived, designed, and planned the study. MR and MB innovated study methods. The data were analyzed by TAL and TR. TAL and TR interpreted the results. TAL wrote the first draft of the article. AHS, TPL, TR, MB and MR critically revised the article, adding important intellectual content. All authors read and approved the final manuscript. 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Makate M, Whetton S, Tait RJ, Dey T, Scollo M, Banks E, et al. Tobacco Cost of Illness Studies: A Systematic Review. Nicotine Tob Res. 2020 Apr 17;22(4):458–65. Rice DP. Cost of illness studies: what is good about them? Inj Prev. 2000 Sep 1;6(3):177–9. WHO. WHO guide to identifying the economic consequences of disease and injury. Geneva: WHO, 2009; 2009. Pearce A. Productivity Losses and How they are Calculated [Internet]. Cancer Research Economics Support Team (CREST); 2016. Available from: https://www.uts.edu.au/sites/default/files/2019-04/crest-factsheet-productivity-loss.pdf Pike J, Grosse SD. Friction Cost Estimates of Productivity Costs in Cost-of-Illness Studies in Comparison with Human Capital Estimates: A Review. Appl Health Econ Health Policy. 2018 Dec;16(6):765–78. Bhattacharjee A, Dolton P, Mosley M, Pabst A. The Fiscal Costs and Benefits of Problem Gambling: Towards Better Estimates [Internet]. London: National Institute of Economic and Social Research; 2023. Available from: https://www.niesr.ac.uk/wp-content/uploads/2023/04/The-Fiscal-Costs-and-Benefits-of-Problem-Gambling.pdf Joyce, James, Zalta, Edward N. (ed). Bayes’ Theorem. In: The Stanford Encyclopedia of Philosophy [Internet]. Fall 2021 Edition. Available from: https://plato.stanford.edu/archives/fall2021/entries/bayes-theorem Salonen AH, Hellman M, Latvala T, Castrén S. Gambling participation, gambling habits, gambling-related harm, and opinions on gambling advertising in Finland in 2016. Nord Stud Alcohol Drugs. 2018 Jun;35(3):215–34. Kela. Disability pension and rehabilitation subsidy. 2017. Disability pension and rehabilitation subsidy. Available from: https://www.kela.fi/web/en/disability-pension-and-rehabilitation-subsidy WEO. Implied PPP conversion rate [Internet]. 2024. Available from: https://www.imf.org/external/datamapper/PPPEX@WEO/FIN WEO. Frequently Asked Questions [Internet]. 2024. Available from: https://www.imf.org/en/Publications/WEO/frequently-asked-questions#4q5 Kontto J, Tolonen H, Salonen AH. What are we missing? The profile of non-respondents in the Finnish Gambling 2015 survey. Scand J Public Health. 2020 Feb;48(1):80–7. Browne M, Rockloff MJ. Prevalence of gambling-related harm provides evidence for the prevention paradox. J Behav Addict. 2018 Jun;7(2):410–22. Dowling NA, Merkouris SS, Greenwood CJ, Oldenhof E, Toumbourou JW, Youssef GJ. Early risk and protective factors for problem gambling: A systematic review and meta-analysis of longitudinal studies. Clin Psychol Rev. 2017 Feb;51:109–24. Hing N, Russell A, Tolchard B, Nower L. Risk Factors for Gambling Problems: An Analysis by Gender. J Gambl Stud. 2016 Jun;32(2):511–34. Sharman S, Butler K, Roberts A. Psychosocial risk factors in disordered gambling: A descriptive systematic overview of vulnerable populations. Addict Behav. 2019 Dec;99:106071. Castrén S, Kontto J, Alho H, Salonen AH. The relationship between gambling expenditure, socio-demographics, health-related correlates and gambling behaviour-a cross-sectional population-based survey in Finland: Gambling expenditure in relation to net income. Addiction. 2018 Jan;113(1):91–106. Grönroos T, Kouvonen A, Kontto J, Salonen AH. Socio-Demographic Factors, Gambling Behaviour, and the Level of Gambling Expenditure: A Population-Based Study. J Gambl Stud [Internet]. 2021 Oct 4 [cited 2022 Feb 16]; Available from: https://link.springer.com/10.1007/s10899-021-10075-6 Tables Table 1. Sociodemographic factors and percentage of people on long-term work disability based on perceived past problem gambling* All % (N=5,122) Past problem gambling * (n=130) Sex Male 49.4 (2,368) 4.4 (95) Female 50.6 (2,754) 1.4 (35) χ 2 -test p<0.001 Age 18-25 16.0 (955) 1.8 (17) 26-40 32.8 (1,474) 5.0 (67) 41-64 51.2 (2,693) 1.8 (46) χ 2 -test <0.001 Education Low 14.9 (599) 3.0 (16) Medium 44.1 (2,127) 3.6 (70) High 41.1 (2,396) 2.0 (44) χ 2 -test 0.002 Disposable income per year 0-15,600 24.6 (1,272) 3.3 (38) 15,700-25,400 26.5 (1,273) 3.3 (36) 25,500-34,100 25.5 (1,255) 2.5 (29) 34200 or more 23.4 (1,265) 2.4 (27) χ 2 -test 0.372 Long-term work disability 6.1 (293) 11.5 (14) χ 2 -test 0.004 % from weighted, N from unweighted data * Self-perceived problem gambling before long-term work disability Table 2 . Methods for calculating l ong-term work disability (LTWD) costs associated with past problem gambling (PG ) 1a) Causality adjustment factor (80%) Calculation Number of people with past PG in study region A 37,405 Share of past PG among people on LTWD B 0.053 Estimated number of people on LTWD with past PG N g =AxB 1982 Average cost of productivity loss due LTWD (€) C pl_avr 35,920.71 Cost of productivity loss due to LTWD associated with past PG in million € (Int$/adult) # N g * 0.8* C pl_avr 56.97 (11.63) 1b) Causality adjustment factor (50%) Cost of productivity loss due LTWD associated with past PG in million € (Int$/adult) # N g * 0.5* C pl_avr 35.61 (7.27) 2) Excess cost Number of people with past PG expected to be on LTDW if they had same rate of LTWD as the general population N p =A*6.1% 2282 Estimate of the number of people with past PG on LTWD based on the research estimate of the number of people on LTWD N h =N p *OR 1 5818 Estimate of the number of people on WD associated with past PG only N=N h -N p 3537 Cost of productivity loss due WD associated with past PG in million € (Int$) # N*C pl_avr 127.04 (25.94) 3) Method based on Bayes' Theorem Average cost of productivity loss due LDWD among people with past PG (€) C pl_avr 37,595.28 Share of past PG in study region PG past 0.028 Share of past PG among people on LTWD B 0.053 Overall rate of people with long-term WD in study region DP 0.061 Share of long-term WD associated with past PG P=DP* B/ PG past 0.115 Cost of productivity loss due long-term WD associated with past PG in million € (Int$/adult) # N g * C pl_avr *P 8.57 (1.75) 1 based on logistic regression OR=2.55 for past problem gambling # converted in 2016 international dollars using World Bank conversion factors Additional Declarations No competing interests reported. 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Latvala","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIie3RMQrCMBSA4RcCZom6vmLRK0RcHJRexVJwFMHFwUEQ4hLpBcQ7uHSOFOziIeri5OCoUMFIXVwaR4f8Q5IhH3kQAJfrH6Nkadb++0g0zGHUAJLrKsFLgsDNquEkRjWgopqU24cQWZLKuQJGZX4H7AQY5fqxKyaGENtg664C7Coci8MmETNDwEYkckCiuBa6nohQQlNbiVcABopnt8Nz+yY/vNIyr4SKKZHWl7+Q1BBfYKQyNU39Yy+U1EJYnF2863wwXK/Y/nxdtMOYreitipR9fQS133e5XC6XpRcyFUEeERjZ0gAAAABJRU5ErkJggg==","orcid":"","institution":"Finnish Institute for Health and Welfare","correspondingAuthor":true,"prefix":"","firstName":"Tiina","middleName":"A.","lastName":"Latvala","suffix":""},{"id":309845831,"identity":"306b8d5b-f6b4-4b84-a760-611171b57ca4","order_by":1,"name":"Matthew Rockloff","email":"","orcid":"","institution":"Central Queensland University","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Rockloff","suffix":""},{"id":309845832,"identity":"4880767f-531b-466c-99d1-aabac2151bde","order_by":2,"name":"Matthew Browne","email":"","orcid":"","institution":"Central Queensland University","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Browne","suffix":""},{"id":309845833,"identity":"4e80b771-1135-4c30-8f94-265cc1193d71","order_by":3,"name":"Tomi Roukka","email":"","orcid":"","institution":"Finnish Institute for Health and Welfare","correspondingAuthor":false,"prefix":"","firstName":"Tomi","middleName":"","lastName":"Roukka","suffix":""},{"id":309845834,"identity":"080ae412-c0a7-41c0-bf13-ed5ef1cc5ae3","order_by":4,"name":"Tomi P. 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Salonen","email":"","orcid":"","institution":"Finnish Institute for Health and Welfare","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"H.","lastName":"Salonen","suffix":""}],"badges":[],"createdAt":"2024-05-27 06:54:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4482877/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4482877/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-24043-x","type":"published","date":"2025-08-19T16:29:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89847284,"identity":"b32ffcc1-427f-4293-9560-be77d73dfedc","added_by":"auto","created_at":"2025-08-25 16:42:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":982611,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4482877/v1/4644d472-c129-49ed-b646-405ddc458495.pdf"},{"id":58251967,"identity":"077192c2-f22c-46db-b5dc-3c5ea468232c","added_by":"auto","created_at":"2024-06-13 03:45:44","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":17921,"visible":true,"origin":"","legend":"","description":"","filename":"costmodelsupplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4482877/v1/72d91c4dca0badd7b284aca4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Towards more reliable gambling cost estimates, a population-based study with register-linkage","fulltext":[{"header":"Background","content":"\u003cp\u003eGambling availability has increased during previous decades because of growth in internet sites which have enabled gambling from home, work and nearly anywhere. Gambling is a common form of entertainment in many western countries, and most of adults have participated in it at least sometime of their life (1,2). However, for some people gambling can cause serious health, financial and interpersonal harms (3,4). Financial harms, such as lost savings and debt problems, are the most common harms reported by gamblers (5). Gambling-related financial harms can in turn increase psychological distress, substance use, relationship problems, crime, and even suicidality (4,6). Based on the Lotteries Act, the aims of the Finnish gambling monopoly system are to prevent and reduce gambling-related financial, social and health-related harm (7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere is strong evidence that different types of health problems are linked with problem gambling, and its most severe form, gambling disorders (GD) (e.g. 8,9). It is clear that more severe morbidity tends to cause higher expenses. Accordingly, the need to calculate gambling costs has been identified in several countries (10). Browne and colleagues (11) found in their systematic literature review on gambling-related harms and costs that only three out of 36 gambling-prevalence studies published in 2010\u0026ndash;2016 reported gambling costs. The most cited and emulated research in the field of gambling costs studies found that the overall gambling costs in Australia were 1.8\u0026ndash;5.6 billion Australian dollars (AUD) between 1997 and 1998 (12). A more recent study from Australia estimated cost to be $7 billion in 2014\u0026ndash;2015 in Victoria (11). Other studies conducted in Europe (8,13\u0026ndash;18) and Asia (19) have also identified significant costs to society. However, due to a range of differences in gambling environments and policies, and different methods behind these calculations, comparison of costs between countries is not straight-forward.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere is also a lack of consensus on what should be included when calculating gambling costs\u0026nbsp;(20,21). There has been also discussion whether intangible (pain and suffering) costs, which are more difficult to evaluate in monetary form, should be included in calculations. While these questions are not easily resolvable, it is nevertheless advisable to pay attention to the methods how gambling costs are calculated. Regardless of past research showing a strong association between gambling and harms, relatively few studies have examined costs of these harms to society. Thus, we will start by reviewing methods used in the gambling cost studies and evaluate them critically. Our emphasis is on tangible costs, where a monetary value can be most readily applied. This is not to deny the importance of other costs, but rather to make our evaluation more tractable. This review informed our evaluation of alternative methods for calculations that follow later in this paper.\u003c/p\u003e\n\u003ch1\u003e\u003cstrong\u003e\u003cem\u003eGambling and health issues\u003c/em\u003e\u003c/strong\u003e\u003c/h1\u003e\n\u003cp\u003eThere is strong evidence on that psychiatric and substance used disorders are linked with gambling problems (e.g. 22\u0026ndash;28). A Swedish registry-based study indicated that 73 percent of those who had a diagnosed GD had other co-occurring psychiatric diagnosis (23). A meta-analysis showed that the highest mean prevalence for co-occurring psychiatric disorders were for nicotine dependence (60.1%), followed by a substance use disorder (57.5%), mood disorders (37.9%) and anxiety disorders (37.4%) (25). Overall, self-rated health is found to be lower among those with problem gambling than non-gambling counterparts (29,30).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere is evidence of comorbidity between problem gambling and poor physiological health\u0026nbsp;(9,29\u0026ndash;32).\u0026nbsp;Problem gambling is linked to poor diet, low physical exercise, and obesity\u0026nbsp;(9,29).\u0026nbsp;Moreover,\u0026nbsp;those with gambling disorder are more likely than low-risk individuals to be diagnosed with tachycardia, angina, cirrhosis, and other liver\u0026nbsp;disease\u0026nbsp;(31). In addition, problem\u0026nbsp;gambling is associated with headache, fatigue, and sleeping problems\u0026nbsp;(33). Among women, problem gambling is linked to bronchitis, fibromyalgia, and migraine\u0026nbsp;(34)\u0026nbsp;and among older adults, with heart conditions\u0026nbsp;(32).\u0026nbsp;There is also evidence that individuals with GD have an increased risk of receiving sickness allowance\u0026nbsp;(35)\u0026nbsp;and increases risk of work disability\u0026nbsp;(36).\u003c/p\u003e\n\u003cp\u003eIn Finland the most common reasons for receiving sickness allowance and disability pension are mental and behavioral disorders. Mental and behavioral disorders account for over half of disability pensions and over one third of sickness allowances\u0026nbsp;(37). The likelihood of transitioning to disability pension increases significantly after the first year of receiving sickness allowance\u0026nbsp;(38), with the risk escalating further with the duration of the sickness absence\u0026nbsp;(39). Disability pension poses a substantial financial burden on society due to the low rates of recipients returning to work\u0026nbsp;(40).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCost of illness (COI) studies\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCost of illness (COI) is defined as the value of the resources that are lost because of a health problem. COI studies assess the economic burden of health problems on the population overall (41). COI studies can be used to draw public attention to particular health issues and to provoke policy debate (42). They can also guide planning of healthcare and preventive services, interventions and the evaluation of different policies (43\u0026ndash;45).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCOI studies can be based on the incidence or prevalence of the disease. In an incidence-based approach, the new (incident) cases are measured, while the prevalence-based approach measures the existing (new and pre-existing cases) cases over a specified period, usually one year. It is considered that prevalence-based approach is more appropriate for assessing total current economic burden of a health problem whereas an incidence-based approach is more useful for estimating the expected impact in the future\u0026nbsp;(46). In this study we applied prevalence-based approach.\u003c/p\u003e\n\u003cp\u003eCOI studies commonly include related healthcare costs and other resources used (direct costs), losses of productivity related to morbidity and mortality (indirect costs), and the losses in quality and length of life (intangible costs)\u0026nbsp;(43). Traditionally these effects of health problem are converted into monetary values wherever possible\u0026nbsp;(41,44). However, intangible costs are not usually monetized; instead they are expressed measures, such as disability-adjusted life-years (DALYs) or quality-adjusted life-years (QALYs)\u0026nbsp;(41).\u003c/p\u003e\n\u003cp\u003eAlthough debate regarding the most appropriate approach to calculate productivity losses is still on-going, we opted to use the human capital approach\u0026nbsp;(47). The value of the human capital is estimated based on the value of an average individual\u0026apos;s future earnings. On the other hand, the fractional costs approach attributes only 80% of losses to avoid potential overestimation of indirect costs\u0026nbsp;(48). This discounting is intended to account for the fact that it cannot be assumed that the condition plays a 100% causal role in impacting earnings. However, given the true causal role is unknown, any such discounting is unavoidably somewhat arbitrary.\u003c/p\u003e\n\u003ch1\u003e\u003cstrong\u003e\u003cem\u003eMethods commonly used in gambling cost studies\u003c/em\u003e\u003c/strong\u003e\u003c/h1\u003e\n\u003ch2\u003e\u003cem\u003eCausality adjustment factors\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eAs mentioned earlier, the most emulated method is from the Australian Productivity Commission\u0026nbsp;(12), and it is still widely used. The main principle behind this method is that cost of the harm per gambler is multiplied by the number of people experiencing the harm. Because the causality between gambling and harms is often unknown, costs have been discounted with a \u0026lsquo;causality adjustment factor\u0026rsquo; (CAF)\u0026nbsp;(12). This based on expert opinions suggesting that approximately 20% of individuals struggling with gambling issues would have encountered similar personal and family-related consequences even in the absence of gambling problems. In practice, this entails that costs are discounted by 20 percent (multiplied by 0.8), similar to the fractional cost approach described above. In some studies costs were discounted by as much as 50 percent, as there were no or only little evidence on the direction of causality\u0026nbsp;(8). The costs are calculated as following,\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCost of harm = N\u003csub\u003eG\u003c/sub\u003e * CAF* C, where \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/em\u003e(1.1)\u003c/p\u003e\n\u003cp\u003eN\u003csub\u003eG\u003c/sub\u003e=\u0026nbsp;estimated number of gamblers with a particular harm\u003c/p\u003e\n\u003cp\u003eCAF= causality adjustment factor\u003c/p\u003e\n\u003cp\u003eC=unit cost of individual with harm.\u003c/p\u003e\n\u003cp\u003eDespite acknowledging the limitations of discounting costs by an arbitrary CAF, many studies have used the method\u0026nbsp;(18).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eExcess costs\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThree research reports conducted in Britain\u0026nbsp;(15,17,49)\u0026nbsp;quantified costs by calculating the excess costs between gamblers compared to the non-gambler population and then multiplying this excess cost by unit cost of individual harm as following:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCost of harm = (N\u003csub\u003eG\u0026nbsp;\u003c/sub\u003e- N\u003csub\u003eP\u003c/sub\u003e) * C,\u0026nbsp;\u003c/em\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(1.2) where\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eN\u003csub\u003eG\u003c/sub\u003e\u003c/em\u003e=\u0026nbsp;estimated number of gamblers with a particular harm\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eN\u003csub\u003eP\u003c/sub\u003e\u003c/em\u003e= estimated number of gamblers expected to have harm if they had same rate of harm as the general population\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eC\u003c/em\u003e=unit cost of individual with harm.\u003c/p\u003e\n\u003cp\u003eThis Excess cost does not use actual data from real cases. Estimated number of gamblers expected to have experienced a particular harm is calculated by multiplying prevalence rate of a particular harm by the prevalence figures for problem gambling. This gives an estimate of the number of gamblers with specific harms in the general adult population. Multiplying these figures by the estimate of association between gambling and these specific harms (adjusted-OR) produces the number people of experiencing problem gambling who are expected to have these particular harms. The difference of estimated number and expected number of specific harms, (NG \u0026minus; NP), will give an estimate of the number of people with a particular harm associated with problem gambling only.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMethod based on Bayes\u0026apos; Theorem\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBayes\u0026apos; Theorem is a mathematical formula used for calculating conditional probabilities (50). In its simplest form, Bayes\u0026apos; Theorem is expressed as:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 375px; height: 82.9108px;\" width=\"375\" height=\"82.9108\"\u003e\u003c/p\u003e\n\u003cp\u003eP(A∣B) is the posterior probability of event A given that event B has occurred.\u003c/p\u003e\n\u003cp\u003eP(B∣A) is the likelihood of event B occurring given that event A has occurred.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e(\u003cem\u003eA\u003c/em\u003e) and P(B) are the prior probabilities of events A and B respectively.\u003c/p\u003e\n\u003cp\u003eWe can use Bayes\u0026apos; Theorem to estimate the proportion of those experiencing long-term work disability who likely had their problem gambling lead to the work disability:\u003c/p\u003e\n\u003cp\u003eP(gambling led to pension) = [P(past gambling problems | long-term work disability) * P(long-term work disability)] / P(past gambling problems)\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cp\u003eP(past gambling problems | work disability) is the rate of gambling problems before long-term work disability had started among those who are currently on long-term work disability\u003c/p\u003e\n\u003cp\u003eP(disability pension) is the overall rate of long-term work disability in the population in 2016\u003c/p\u003e\n\u003cp\u003eP(past gambling) is the rate of gambling problems before long-term work disability had started in the general population\u003c/p\u003e\n\u003cp\u003eIf P(past gambling problems | work disability) is significantly higher than P(past gambling), it suggests that past gambling problems increase the likelihood of long-term work disability. Formula 1.3 provides the proportion of people with long-term work disability whose gambling issues likely occurred before and played a role in their extended work incapacity.\u003c/p\u003e\n\u003cp\u003eTo conclude, during the past decade there has only been limited discussion of the appropriate method for calculating gambling costs. Based on the extant literature, we have selected three methods, including the Causality adjustment factors (with two variations), the Excess costs and the Method based on Bayes\u0026apos; Theorem, and use them in our gambling costs calculations. Our calculations focus on the indirect costs regarding to long-term work disability, in those aged 18\u0026ndash;64 years who experienced gambling problems before long-term work disability started. This is a specific context for gambling cost assessment for which high quality data is available, should provide good scope for evaluating alternative costing methods. We do not mean to suggest, however, that these are the only costs \u0026ndash; financial or otherwise \u0026ndash; that can be due to gambling issues. Rather, the goal of this study is to provoke discussion on methods used in gambling cost studies and hopefully assist the formulation of a consistent approach for cost calculations in the field of gambling studies.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eData and participants\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study utilized the population-based Gambling Harms survey, which was conducted by the Finnish Institute for Health and Welfare among residents of three geographical areas in Finland: Uusimaa, Pirkanmaa and Kymenlaakso in 2016\u0026nbsp;(51). People in these areas comprise 42% of the Finnish population. Statistics Finland collected the data between January and March in 2017, but all the gambling-related questions reflected activity in the calendar year 2016.\u003c/p\u003e\n\u003cp\u003eParticipants had to be 18 years old or over, and they needed to understand Finnish or Swedish, as the online and postal surveys were available in these two official languages. Institutionalized persons (prisoners, infirmed, etc.) were excluded from the survey. From the population register, 20,000 potential participants were randomly selected. When non-eligible (n = 67) participants were excluded (e.g., illness, living abroad), the study sample size was 19,933 persons. Overall, 7,186 adults participated in the study, giving a response rate of 36.1%.\u003c/p\u003e\n\u003cp\u003ePotential participants were sent an invitation letter at their home addresses, wherein they received written information about the study and the principles of voluntary participation. They were informed that participating the study involved the register linkage, whereby government-held information on them would be combined with their survey results. Information was also provided about the registrars and a list of the register-based variables used in analyses. They also were informed that their responses would be used for scientific purposes. The invitation letter included a link to the online survey and personal participation code. Most respondents (71%, n= 5,084) participated using the online survey.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRespondents\u0026rsquo; ages ranged from 18 to 94 years (M=50.5, SD=18.8) and 47.7% of them were male. Based on the sample frame, respondents who were older, women, married, or had higher education were more willing to participate than men, younger, divorced, or single, or those with lower-than-average education\u0026nbsp;(51). The respondents most willing to participate were in the age groups 55\u0026ndash;64 and 65\u0026ndash;74, while the youngest age group, 18\u0026ndash;24-year-olds, and particularly men, were least responsive\u0026nbsp;(51). For the purposes of this study, only working age respondents (18\u0026ndash;64-year-olds) were selected (N = 5,122). The data were weighted on gender, age, and region of residence.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eMeasures from the survey\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003ePerceived past problem gambling was evaluated using a single question: \u0026ldquo;Do you feel that gambling has ever been a problem for you?\u0026rdquo;. If they answered yes, then they were asked the most recent year, when they felt that was the case. Those respondents who felt that they had experienced gambling problems before long-term work disability had started, were treated has having experienced past problem gambling.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eMeasures from the register data\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe survey data were linked with the register data administered by Statistics Finland. Register data included information on sociodemographic measures (sex, age, education and disposable income), and information on disability pensions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEducation\u003c/em\u003e. Education was based on the highest degree attained and followed the International Standard Classification of Education. Those who had missing values were coded as \u0026lsquo;low/unknown education\u0026rsquo; (below Level 3). Levels 3 and 4 were classified as \u0026lsquo;medium education\u0026rsquo; and Level 5 or higher as \u0026lsquo;high education\u0026rsquo;.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDisposable income per year\u003c/em\u003e. Disposable income is obtained by adding current transfers receivable to primary income and by deducting all current transfers payable (Table 1). Incomes are rounded to the nearest hundred euros. In analyses disposable income was divided to quartiles.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLong-term work disability\u003c/em\u003e. Based on the Finnish social security system, if a person is incapable for work, he/she can receive sickness allowance as compensation for loss of income for maximum of 300 working days (about a year). Sickness allowance is available after completing a waiting period, which consists of the first day of illness and the following nine working days. After receiving sickness allowance for 150 working days, a person will be informed about the availability of rehabilitation and the process of applying for a pension. If rehabilitation does not restore or improve the work ability, a person may be entitled to a disability pension\u0026nbsp;(52).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the register data, all sickness days compensated to receiver or employer were presented. As the sickness allowance is available after a waiting period, nine days were added to the length of compensated sickness days. Days were also divided by 30 to get the number of sickness months.\u003c/p\u003e\n\u003cp\u003eIf respondents had received a disability pension in 2016 a value \u0026ldquo;1\u0026rdquo; was given to him/her and \u0026ldquo;0\u0026rdquo;, if not. There were also variables, which indicated the year and month when disability pension had started. As our purpose was examine only productivity losses in 2016, pensions that had started before year 2016 we given value 12 (12 months in year).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo calculate the total months of long-term work disability, sickness and disability months were summed up. Long-term disability was dichotomized, and respondents were given value of 1 if she/he had been 90 net days or more on sickness absence or disability pension during 2016, as in a previous study (37).\u003c/p\u003e\n\u003cp\u003eBased on the average length of long-term work disability in months (dm\u003csub\u003eavr\u003c/sub\u003e), the average cost for productivity loss associated with one persons\u0026rsquo; absence from work because of long-term work disability (C\u003csub\u003epld_avr\u003c/sub\u003e) was calculated as follows:\u003c/p\u003e\n\u003cp\u003eC\u003csub\u003epld_avr\u003c/sub\u003e\u003cem\u003e\u0026nbsp;= dm\u003csub\u003eavr\u003c/sub\u003e*w\u003csub\u003em\u003c/sub\u003e+0.2(dm\u003csub\u003eavr\u003c/sub\u003e*w\u003csub\u003em\u003c/sub\u003e), where \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e(1.3)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003edm\u003csub\u003emed\u003c/sub\u003e\u003c/em\u003e= average length of long-term work disability in months\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ew\u003csub\u003em\u003c/sub\u003e\u003c/em\u003e= average of monthly salary estimated by calculating the mean of 16 years\u0026apos; salaries\u003c/p\u003e\n\u003cp\u003eAverage monthly salary was estimated by calculating the mean of 16 years\u0026apos; average wages. This was \u0026euro; 2771.66 between 2000-2016. An estimate of the value of the productivity loss was obtained by adding the side costs of the salary, i.e. the employer\u0026apos;s social insurance fees, the amount of which is approximately 20 percent of the salary (Lappo, 2023).\u003c/p\u003e\n\u003cp\u003eThe average length of work disability in months was 10.8. This gave a unit cost of \u0026euro; 35,920.71 for productivity loss due to work disability. This average cost was applied in Causality adjustment factor-method and in Excess cost-method. In method based on Bayes\u0026rsquo; theorem dm\u003csub\u003emed\u0026nbsp;\u003c/sub\u003ewas replaced with actual length of disability as follows:\u003c/p\u003e\n\u003cp\u003eC\u003csub\u003epl_avr\u003c/sub\u003e\u003cem\u003e\u0026nbsp;= dm*w\u003csub\u003em\u003c/sub\u003e+0.2(dm*w\u003csub\u003em\u003c/sub\u003e), where \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e(1.4)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003edm\u003c/em\u003e= length of work disability in months\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eRespondents\u0026rsquo; sociodemographic factors, gambling severity, and the percentage of those respondents on disability pension\u0026nbsp;are presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCausality adjustment factor -method and Excess cost -method\u003c/em\u003e: First, logistic regression models were created, where age, sex, disposable income and education were set as covariates to examine whether there were statistically significant association between past problem gambling and long-term work disability (Supplementary material, Table S1). All respondents who were on long-term work disability were given value \u0026lsquo;1\u0026rsquo; and all others value \u0026lsquo;0\u0026rsquo;. \u0026nbsp;Long-term work disability was dependent variable. Recreational gambling was set as the reference group. Subsequently, formula 1.1 and 1.2 were used to calculate the productivity losses associated with past problem gambling. Both causality adjustment factors 0.8 and 0.5 were used. The results are presented in Table 2 in euros and in 2016 international dollars (Int$) per adult (person age of 20 or more) using the Purchasing Power Parity (PPP) exchange rate\u0026nbsp;(53). The PPP exchange rate between two countries signifies the rate at which the currency of one country must be converted into that of another to maintain parity in purchasing power. In the World Economic Outlook (WEO) online database, the implied PPP conversion rate is expressed as national currency per current international dollar (Int$)\u0026nbsp;(54). Int$ are a hypothetical unit of currency used in international comparisons of purchasing power.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethod based on Bayes\u0026apos; Theorem\u003c/em\u003e: Productivity loss for each respondent was calculated by formula 1.3. This formula provides the proportion of people with long-term work disability whose gambling issues likely occurred before and played a role in their extended work incapacity. The average cost of productivity loss associated with long-term work disability among people with past PG is presented in Table 2 in euros and in Int$.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analyses were done using IBM SPSS Statistics for Windows version 27.0.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOf the respondents, 2.8% had experienced past problem gambling. Further, 5.1% of the respondents were on long-term work disability (Table 1). Of the respondents who experienced past problem gambling, 11.5% were on long-term work disability (Table 1). In logistic regression models where respondents\u0026rsquo; sex, age, disposable income, and education were adjusted, statistically significant association between long-term work disability and past problem gambling were seen (Supplementary material, Table S1).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eCost based on Causality adjustment factor -method\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eIt was estimated that in study regions there were 37,405 people experiencing past problem gambling (Table 2). Among those on long-term work disability, 5.3% experienced past problem gambling (N = 1,982). Based on the CAF method (formula 1.1) and estimation on a unit cost of \u0026euro; 35,920.71 for productivity loss due to long-term work disability, this would mean that productivity losses associated with past problem gambling would be 56.97 million euros (11.63 Int$/adult). If CAF was replaced with value 0.5 the productivity losses would have been 35.61 million euros (7.27 Int$/adult) for past problem gambling (Table 2).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eCost based on Excess cost -method\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eMultiplying the prevalence rate of being on long-term work disability in the survey region (6.1%) by the estimated number of people experiencing past problem gambling (A=37,405) gives an estimate of expected number of past gamblers on long-term work disability if they had same rate of harm as the general population (N\u003csub\u003ep\u003c/sub\u003e=2,282) (Table 2). When this figure is multiplied by the estimate of association between past gambling and long-term work disability (OR\u003csub\u003eproblem\u003c/sub\u003e=2.55), it yields the estimated number of gamblers receiving disability pension (N\u003csub\u003eh\u003c/sub\u003e=5,818). The difference of the estimated number and the expected number gives an estimate of the number of people on long-term work disability associated with past problem gambling only (N=3,537). When this number is multiplied by the average cost of productivity loss due long-term disability, it gives the cost of 127.04 million euros (25.94 Int$/adult) for past problem gambling (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eCost based on Bayes\u0026apos; Theorem\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe percentage of gambling problems before long-term work disability had started among those who were currently on long-term work disability was 5.3 (Table 2). This was significantly higher than percentage of gambling problems before long-term work disability had started in the general population (\u0026chi;\u003csup\u003e=\u003c/sup\u003e8.22(1), p=0.004). The average productivity loss due to long-term work disability among people experiencing past gambling problems was \u0026euro; 37,595.28. Based on the Bayes\u0026apos; Theorem (formula 1.3) we get the estimate for the proportion of those on long-term work disability who likely had their gambling lead to the disability. In approximately 11.5% of long-term work disabilities, gambling issues likely preceded and thus contributed to disability. Thus, productivity losses associated with past problem gambling are estimated at 8.57 million euros (1.75 Int$/adult).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study examined the economic burden associated with past problem gambling using three different methods among Finnish population aged 18\u0026ndash;64 years. The study focused on indirect cost regarding productivity losses due to long-term work disability among people experiencing problem gambling before work disability started. As mentioned above, our purpose was not to examine the overall costs of gambling, but rather to evaluate different approaches for one specific outcome: productivity losses resulting from the uptake of disability pensions. Two of the methods, the CAF -method and the Excess cost method, are most used in gambling field, whereas the method based on Bayes\u0026rsquo; theorem was a novel way to estimate gambling cost. Overall, these three methods gave very different estimates on costs associated with past problem gambling. The Excess cost method gave the highest estimate of 127.04 million euros (25.94 Int$/adult), followed by the Causality adjustment method (80%) showing 56.97 million euros (11.63 Int$/adult), and the CAF 50% estimated at 35.61 million euros (7.27 Int$/adult). This is a natural consequence of the arbitrary choice for the discounting factor. The method based on Bayes\u0026rsquo; theorem gave the lowest estimation of the cost, calculating 8.57 million euros (1.75 Int$/adult), which is over 14 times lower than the highest estimate. Thus, it appears that CAF methods or Excess cost methods are generally likely to give higher estimates on gambling costs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDetermining the extent to which gambling is the direct cause of various social and economic harms is challenging. The CAF methods can lead to inflated estimates of gambling-related costs as it attempts to adjust for the proportion of observed harms attributable to gambling just by multiplying cost by 0.8 or by 0.5. This adjustment can be imprecise, often resulting in higher cost estimates that may not accurately reflect the true impact of gambling. Similar problems arise with the Excess Cost method, as it estimates the number of people experiencing a particular harm associated with problem gambling by comparing the observed number of affected individuals to the expected number in the general population. This approach can lead to inaccuracies because it assumes that any excess harm among gamblers is directly attributable to gambling, without adequately accounting for other factors that might contribute to these harms. Further, both Excess cost and CAF method often rely on average values, which can obscure the significant variations in gambling behavior and its impacts. Utilization of the Bayes\u0026rsquo; theorem allowed us to leverage the data on temporal patterns of gambling problems to estimate the plausible proportion where gambling was the precipitating factor for long-term work disability, rather than the other way around. Using this approach, we estimated that approximately 11.5% costs related to long-term work disability was due to past gambling problems.\u003c/p\u003e\n\u003cp\u003eOverall, reliable data are required for reliable estimates on gambling related costs. The data must be from a representative sample of the general population. High response rates are crucial for being able to generalize about the population. This is especially important to consider since there are declining trends in response rates. Low response rates may cause biased findings. Methods for controlling bias would offer useful information about the impact of non-response on the results\u0026nbsp;(55). Information on gambling related costs should rely on multiple data sources. Besides population-based surveys, also health register data and player account data should be utilized.\u0026nbsp;An especially crucial aspect to explore would involve examining the trajectories of gambling through the analysis of longitudinal data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile our study examined only problem gambling, it is essential to recognize gambling as a continuum when examining gambling-related costs. We should not solely focus on problem gambling but consider the entire spectrum. It is true that the most serious forms of harms, like suicide attempts, large debts, and criminal activity are more common among people who are gambling at the highest problem level. However, a greater number of cases, and thus higher costs, come from at-risk gamblers within a population. Although they are harmed less on an individual level, they are much more prevalent than people gambling at problem level\u0026nbsp;(56). Further, problem gambling is not static condition; individuals can move along a continuum from less severe levels of gambling behavior to more problematic ones, and vice versa. Factors such as personal circumstances, social influences, access to gambling opportunities, and changes in mental health can all play a role in this progression or regression along the continuum.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFuture studies should also recognize different subgroups. Based on several review articles and meta-analyses, several sociodemographic risk factors, such as male gender, young and old age as well as low socio-economic status are associated with problem gambling\u0026nbsp;(57\u0026ndash;59).\u0026nbsp;People with lower income spend a relatively larger proportion of their household income on gambling. Similarly, the unemployed, people laid off from work and people with lower education spend a larger proportion of their income on gambling\u0026nbsp;(60,61).\u003c/p\u003e\n\u003cp\u003eThere are several limitations in our study. First, our emphasis was not on the total cost of gambling. Instead, our calculations covered one element of productivity losses. We did not have information on severe harms, such as suicidal deaths or incarcerations related to gambling, which would further impact on productivity losses. It is important to note that we have no information on costs associated with presenteeism, and thus productivity declines due to gambling problems may reflect low output amongst workers suffering gambling-related problems. Thus, these results may underestimate some aspects of productivity losses associated with gambling. There were also quite few people experiencing past problem gambling and simultaneous on long-term work disability. This enabled us to concentrate solely on new disability pension that started on 2016. In addition, we cannot exclude confounding factors through differences in unobserved behaviors. The effect of gambling could, therefore, be under- or overestimated, for example by not adjusting for any concomitant diseases, other risk behavior, or socio-demographic characteristics. To minimize potential confounding, further studies should control at least for some concomitant diseases and, for example, high levels of alcohol consumption that often accompany gambling problems (26). We had also some scarcity in register data, as education records had no information on the lowest levels of education. Also, we did not have information on past gambling severity for the person reported having experienced problem gambling. Further we did not consider any age, sex or socioeconomic differences in salaries or the fact that work productivity decreases with age. Thus, in these various respects our calculations were simplified.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn terms of sampling, a further limitation was that we used a cross-sectional sample, and therefore definitive conclusions regarding causal relationships between productivity losses and gambling were not possible.\u0026nbsp;For example, disability pensions could cause increased problem gambling behavior, rather than the other way around because it is plausible that individuals receiving disability pensions may have more free time for gambling due to being unable to work. Some individuals on disability pensions may see gambling as a relief from negative affect or a potential way to supplement their income, especially if they face financial difficulties. Further, we cannot conclusively assert that problem gambling was the sole reason for long-term work disability, unlike the more evident connection observed in cases of alcohol use. In the context of alcohol use, there is a clearer physiological link between excessive consumptions and health conditions potentially leading to disability. However, determining the direct association between problem gambling and long-term work disability may involve more nuanced considerations and additional factors. Finally, the response rate of this study (36%) was relatively low, and thus our results can reflect self-selection biases.\u003c/p\u003e\n\u003cp\u003eRegardless of these limitations, the method based on Bayes\u0026rsquo; theorem, which relied on subject-level data has the advantages when consequences associated with gambling are considered. The former methods used have relied on average costs and arbitrary ways to adjust the unknown direction of causality, which give higher estimates on gambling costs. Although, method based on Bayes\u0026rsquo; theorem gave lower estimates, it nevertheless implies that gambling imposes a significant societal health burden.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur results emphasize that \u0026ldquo;the method matters\u0026rdquo; since the estimates of gambling related costs greatly vary depending on what method is chosen. In the absence of a better estimate, gambling cost studies have commonly used \u0026ldquo;causality adjustment factors\u0026rdquo;, and to some extent it has become as a standard in the field of gambling cost studies, at-least amongst the rare gambling cost studies conducted so far. However, a few studies have diverged from this standard and used the Excess cost method. It appears that methods commonly used in gambling cost studies give higher estimations on gambling costs when these arbitrary causality adjustments methods are used. So far, no consensus exists in the field of gambling studies on how one should estimate gambling related costs. However, utilization of Bayes\u0026rsquo; theorem would allow to leverage the data on temporal patterns of gambling problems to estimate the plausible proportion where gambling is the precipitating factor for the experimented harm, rather than the other way around. One practical solution that could facilitate the comparison of gambling costs between countries is, that the costs would be presented as Int$ per adult using the PPP exchange rate. Importantly, such a consensus would be crucial in order to generate comparable and reliable figures, and overall stronger evidence for informed decision making related to gambling policies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCAF Causality adjustment factors\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCOI Cost of illness\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDALYs Disability-adjusted life-years\u003c/p\u003e\n\u003cp\u003eGD Gambling disorder\u003c/p\u003e\n\u003cp\u003eInt$ international dollars\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePPP Purchasing Power Parity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQALYs Quality-adjusted life-years\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWEO World Economic Outlook\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThroughout the research process, basic principles of the research ethics were applied (The World Medical Association\u0026rsquo;s Declaration Helsinki 2004). The research protocol was approved by the Ethics Committee of the Finnish Institute for Health and Welfare (Statement THL/1390/ 6.02.01/2016). Statistics Finland gave the permission to use the register-based measures, and their rules and instructions were followed. All the statistical analyses on register data were conducted in a protected environment using a remote access system, and the results were screened by Statistics Finland before publishing. While presenting the results, the data were treated with strict anonymity to safeguard the identities of the respondents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe survey data analyzed during the current study are available in the [Finnish Social Science Data Archive] repository, [https://urn.fi/urn:nbn:fi:fsd:T-FSD3261]. Register data is only available by permission from Statistics Finland.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eGambling\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHarms survey and daily work of the authors TAL, TR and AHS at the Finnish Institute for Health and Welfare, Finland, was funded by the Ministry of Social Affairs and Health, Finland, within the objectives of the \u0026sect;52 Appropriation of the Lotteries Act. The funders have had no role in the study design, analysis, or interpretation of the results of the manuscript or any phase of the publication process. Future opportunities of the authors are not contingent upon the results of the research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eTAL, TR, TPL and AHS conceived, designed, and planned the study. MR and MB innovated study methods. The data were analyzed by TAL and TR. TAL and TR interpreted the results. TAL wrote the first draft of the article. AHS, TPL, TR, MB and MR critically revised the article, adding important intellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSassen M, Kraus L, B\u0026uuml;hringer G, Pabst A, Piontek D, Taqi Z. Gambling among adults in Germany: Prevalence, disorder and risk factors. 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Clin Psychol Rev. 2017 Feb;51:109\u0026ndash;24. \u003c/li\u003e\n\u003cli\u003eHing N, Russell A, Tolchard B, Nower L. Risk Factors for Gambling Problems: An Analysis by Gender. J Gambl Stud. 2016 Jun;32(2):511\u0026ndash;34. \u003c/li\u003e\n\u003cli\u003eSharman S, Butler K, Roberts A. Psychosocial risk factors in disordered gambling: A descriptive systematic overview of vulnerable populations. Addict Behav. 2019 Dec;99:106071. \u003c/li\u003e\n\u003cli\u003eCastr\u0026eacute;n S, Kontto J, Alho H, Salonen AH. The relationship between gambling expenditure, socio-demographics, health-related correlates and gambling behaviour-a cross-sectional population-based survey in Finland: Gambling expenditure in relation to net income. Addiction. 2018 Jan;113(1):91\u0026ndash;106. \u003c/li\u003e\n\u003cli\u003eGr\u0026ouml;nroos T, Kouvonen A, Kontto J, Salonen AH. Socio-Demographic Factors, Gambling Behaviour, and the Level of Gambling Expenditure: A Population-Based Study. J Gambl Stud [Internet]. 2021 Oct 4 [cited 2022 Feb 16]; Available from: https://link.springer.com/10.1007/s10899-021-10075-6\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Sociodemographic factors and percentage of people on long-term work disability based on perceived past problem gambling*\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll %\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=5,122)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePast problem gambling\u003csup\u003e\u0026nbsp;*\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=130)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e49.4 (2,368)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e4.4 (95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e50.6 (2,754)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e1.4 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003ep\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e18-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e16.0 (955)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e1.8 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e26-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e32.8 (1,474)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e5.0 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e41-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e51.2 (2,693)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e1.8 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e14.9 (599)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e3.0 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e44.1 (2,127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e3.6 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e41.1 (2,396)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e2.0 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisposable income per year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e0-15,600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e24.6 (1,272)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e3.3 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e15,700-25,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e26.5 (1,273)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e3.3 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e25,500-34,100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e25.5 (1,255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e2.5 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e34200 or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e23.4 (1,265)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e2.4 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong-term work disability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e6.1 (293)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e11.5 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31313131313131%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e% from weighted, N from unweighted data\u003c/p\u003e\n\u003cp\u003e* Self-perceived problem gambling before long-term work disability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. \u003cstrong\u003eMethods for calculating l\u003c/strong\u003e\u003cstrong\u003eong-term work disability (LTWD) costs associated with past problem gambling (PG\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1a) Causality adjustment factor (80%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalculation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of people with past PG in study region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e37,405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eShare of past PG among people on LTWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eEstimated number of people on LTWD with past PG\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003csub\u003eg\u003c/sub\u003e=AxB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e1982\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eAverage cost of productivity loss due LTWD (\u0026euro;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003csub\u003epl_avr\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e35,920.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eCost of productivity loss due to LTWD associated with past PG in million \u0026euro; (Int$/adult)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003csub\u003eg\u003c/sub\u003e * 0.8* C\u003csub\u003epl_avr\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e56.97 (11.63)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1b) Causality adjustment factor (50%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eCost of productivity loss due LTWD associated with past PG in million \u0026euro; (Int$/adult)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003csub\u003eg\u003c/sub\u003e * 0.5* C\u003csub\u003epl_avr\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e35.61 (7.27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2) Excess cost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of people with past PG expected to be on LTDW if they had same rate of LTWD as the general population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003csub\u003ep\u003c/sub\u003e=A*6.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e2282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eEstimate of the number of people with past PG on LTWD based on the research estimate of the number of people on LTWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003csub\u003eh\u003c/sub\u003e=N\u003csub\u003ep\u003c/sub\u003e*OR\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e5818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eEstimate of the number of people on WD associated with past PG only\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003eN=N\u003csub\u003eh\u003c/sub\u003e-N\u003csub\u003ep\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e3537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eCost of productivity loss due WD associated with past PG in million \u0026euro; (Int$)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003eN*C\u003csub\u003epl_avr\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e127.04 (25.94)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3) Method based on Bayes\u0026apos; Theorem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eAverage cost of productivity loss due LDWD among people with past PG (\u0026euro;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003csub\u003epl_avr\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e37,595.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eShare of past PG in study region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003ePG\u003csub\u003epast\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eShare of past PG among people on LTWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eOverall rate of people with long-term WD in study region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003eDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eShare of long-term WD associated with past PG\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003eP=DP* B/ PG\u003csub\u003epast\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.265306122448976%\" valign=\"top\"\u003e\n \u003cp\u003eCost of productivity loss due long-term WD associated with past PG\u0026nbsp;in million \u0026euro; (Int$/adult)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003csub\u003eg\u003c/sub\u003e* C\u003csub\u003epl_avr\u003c/sub\u003e*P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.57 (1.75)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003ebased on logistic regression OR=2.55 for past problem gambling\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e#\u0026nbsp;\u003c/sup\u003econverted in 2016 international dollars using World Bank conversion factors\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"costs, societal costs, problem gambling, population survey, register data, methods","lastPublishedDoi":"10.21203/rs.3.rs-4482877/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4482877/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Although past research has shown a strong association between gambling participation and harms, relatively few studies have attempted to quantify the cost of these harms to society. The need to quantify costs has been identified in several countries, however, no consensus exists in the field of gambling studies on how one should estimate them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Three methods were selected for costs calculations: Causality adjustment factors (with two variations: CAF 80%/ CAF 50%), Excess costs, and a method based on Bayes' Theorem. Our purpose was not to examine the overall costs of gambling, but rather to evaluate different approaches for one specific outcome. Our focus was on indirect costs relating to productivity losses associated with long-term work disability in those aged 18–64 years who had experienced gambling problems before long-term work disability had started. \u0026nbsp;Work disability was operationalized as the net days of sickness absence and disability pension. The study used population-based Gambling Harms survey and the survey data were linked with the register data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e These three methods gave very different estimates on costs relating to productivity losses associated past problem gambling. The Excess cost method gave the highest estimate of 127.04 million euros (25.94 Int$/adult) followed \u0026nbsp;by the Causality adjustment method (CAF80%) of 56.97 million euros (11.63 Int$/adult) and CAF 50% with 35.61 million euros (7.27 Int$/adult). The method based on Bayes' Theorem gave the lowest estimate of the cost at 8.57 million euros (1.75 Int$/adult).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Methods commonly used in gambling cost studies yield higher estimates of gambling costs when arbitrary causality adjustment methods are used. Bayes’ theorem allows leveraging data on temporal patterns of gambling problems to estimate the plausible proportion where gambling is the precipitating factor for the experienced harm, rather than the other way around. Additionally, costs could be presented as Int$ per adult using the PPP exchange rate to facilitate the comparison of gambling costs between countries.\u003c/p\u003e","manuscriptTitle":"Towards more reliable gambling cost estimates, a population-based study with register-linkage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 03:45:39","doi":"10.21203/rs.3.rs-4482877/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-07T19:40:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-27T13:32:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2025-05-16T06:56:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-20T08:52:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14528174145964655001836461544022809580","date":"2024-08-02T14:32:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-26T06:56:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-31T04:49:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-30T11:33:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-30T11:33:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-05-27T06:53:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cd76bb88-0c7a-4b53-b1fb-047874a0dd30","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T16:36:06+00:00","versionOfRecord":{"articleIdentity":"rs-4482877","link":"https://doi.org/10.1186/s12889-025-24043-x","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-08-19 16:29:32","publishedOnDateReadable":"August 19th, 2025"},"versionCreatedAt":"2024-06-13 03:45:39","video":"","vorDoi":"10.1186/s12889-025-24043-x","vorDoiUrl":"https://doi.org/10.1186/s12889-025-24043-x","workflowStages":[]},"version":"v1","identity":"rs-4482877","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4482877","identity":"rs-4482877","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}